
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
import plotly.express as px
import plotly.offline as py

%matplotlib inline


url = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv"
df = pd.read_csv(url)
df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 323700 entries, 0 to 323699
Data columns (total 67 columns):
 #   Column                                      Non-Null Count   Dtype  
---  ------                                      --------------   -----  
 0   iso_code                                    323700 non-null  object 
 1   continent                                   308332 non-null  object 
 2   location                                    323700 non-null  object 
 3   date                                        323700 non-null  object 
 4   total_cases                                 286549 non-null  float64
 5   new_cases                                   314782 non-null  float64
 6   new_cases_smoothed                          313523 non-null  float64
 7   total_deaths                                265511 non-null  float64
 8   new_deaths                                  314824 non-null  float64
 9   new_deaths_smoothed                         313594 non-null  float64
 10  total_cases_per_million                     286549 non-null  float64
 11  new_cases_per_million                       314782 non-null  float64
 12  new_cases_smoothed_per_million              313523 non-null  float64
 13  total_deaths_per_million                    265511 non-null  float64
 14  new_deaths_per_million                      314824 non-null  float64
 15  new_deaths_smoothed_per_million             313594 non-null  float64
 16  reproduction_rate                           184817 non-null  float64
 17  icu_patients                                36791 non-null   float64
 18  icu_patients_per_million                    36791 non-null   float64
 19  hosp_patients                               37673 non-null   float64
 20  hosp_patients_per_million                   37673 non-null   float64
 21  weekly_icu_admissions                       9721 non-null    float64
 22  weekly_icu_admissions_per_million           9721 non-null    float64
 23  weekly_hosp_admissions                      22425 non-null   float64
 24  weekly_hosp_admissions_per_million          22425 non-null   float64
 25  total_tests                                 79387 non-null   float64
 26  new_tests                                   75403 non-null   float64
 27  total_tests_per_thousand                    79387 non-null   float64
 28  new_tests_per_thousand                      75403 non-null   float64
 29  new_tests_smoothed                          103965 non-null  float64
 30  new_tests_smoothed_per_thousand             103965 non-null  float64
 31  positive_rate                               95927 non-null   float64
 32  tests_per_case                              94348 non-null   float64
 33  tests_units                                 106788 non-null  object 
 34  total_vaccinations                          76588 non-null   float64
 35  people_vaccinated                           73341 non-null   float64
 36  people_fully_vaccinated                     69866 non-null   float64
 37  total_boosters                              45062 non-null   float64
 38  new_vaccinations                            63042 non-null   float64
 39  new_vaccinations_smoothed                   172904 non-null  float64
 40  total_vaccinations_per_hundred              76588 non-null   float64
 41  people_vaccinated_per_hundred               73341 non-null   float64
 42  people_fully_vaccinated_per_hundred         69866 non-null   float64
 43  total_boosters_per_hundred                  45062 non-null   float64
 44  new_vaccinations_smoothed_per_million       172904 non-null  float64
 45  new_people_vaccinated_smoothed              172695 non-null  float64
 46  new_people_vaccinated_smoothed_per_hundred  172695 non-null  float64
 47  stringency_index                            197651 non-null  float64
 48  population_density                          274685 non-null  float64
 49  median_age                                  255470 non-null  float64
 50  aged_65_older                               246525 non-null  float64
 51  aged_70_older                               252910 non-null  float64
 52  gdp_per_capita                              250365 non-null  float64
 53  extreme_poverty                             161293 non-null  float64
 54  cardiovasc_death_rate                       250879 non-null  float64
 55  diabetes_prevalence                         263650 non-null  float64
 56  female_smokers                              188175 non-null  float64
 57  male_smokers                                185615 non-null  float64
 58  handwashing_facilities                      122886 non-null  float64
 59  hospital_beds_per_thousand                  221455 non-null  float64
 60  life_expectancy                             297710 non-null  float64
 61  human_development_index                     243170 non-null  float64
 62  population                                  323700 non-null  float64
 63  excess_mortality_cumulative_absolute        11245 non-null   float64
 64  excess_mortality_cumulative                 11245 non-null   float64
 65  excess_mortality                            11245 non-null   float64
 66  excess_mortality_cumulative_per_million     11245 non-null   float64
dtypes: float64(62), object(5)
memory usage: 165.5+ MB


# Use list comprehension to go through every column in df and find the average number of missing values as a percentage
NAN = [(c, df[c].isna().mean()*100) for c in df]

# Convert this list to another dataframe and name columns appropriately
NAN = pd.DataFrame(NAN, columns=["Column Name", "Percentage"])
pd.options.display.max_rows = 67 # So we can see all the rows in NAN
NAN


print(df["location"].unique())
print(len(df["location"].unique()))

['Afghanistan' 'Africa' 'Albania' 'Algeria' 'American Samoa' 'Andorra'
 'Angola' 'Anguilla' 'Antigua and Barbuda' 'Argentina' 'Armenia' 'Aruba'
 'Asia' 'Australia' 'Austria' 'Azerbaijan' 'Bahamas' 'Bahrain'
 'Bangladesh' 'Barbados' 'Belarus' 'Belgium' 'Belize' 'Benin' 'Bermuda'
 'Bhutan' 'Bolivia' 'Bonaire Sint Eustatius and Saba'
 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'British Virgin Islands'
 'Brunei' 'Bulgaria' 'Burkina Faso' 'Burundi' 'Cambodia' 'Cameroon'
 'Canada' 'Cape Verde' 'Cayman Islands' 'Central African Republic' 'Chad'
 'Chile' 'China' 'Colombia' 'Comoros' 'Congo' 'Cook Islands' 'Costa Rica'
 "Cote d'Ivoire" 'Croatia' 'Cuba' 'Curacao' 'Cyprus' 'Czechia'
 'Democratic Republic of Congo' 'Denmark' 'Djibouti' 'Dominica'
 'Dominican Republic' 'Ecuador' 'Egypt' 'El Salvador' 'England'
 'Equatorial Guinea' 'Eritrea' 'Estonia' 'Eswatini' 'Ethiopia' 'Europe'
 'European Union' 'Faeroe Islands' 'Falkland Islands' 'Fiji' 'Finland'
 'France' 'French Guiana' 'French Polynesia' 'Gabon' 'Gambia' 'Georgia'
 'Germany' 'Ghana' 'Gibraltar' 'Greece' 'Greenland' 'Grenada' 'Guadeloupe'
 'Guam' 'Guatemala' 'Guernsey' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti'
 'High income' 'Honduras' 'Hong Kong' 'Hungary' 'Iceland' 'India'
 'Indonesia' 'Iran' 'Iraq' 'Ireland' 'Isle of Man' 'Israel' 'Italy'
 'Jamaica' 'Japan' 'Jersey' 'Jordan' 'Kazakhstan' 'Kenya' 'Kiribati'
 'Kosovo' 'Kuwait' 'Kyrgyzstan' 'Laos' 'Latvia' 'Lebanon' 'Lesotho'
 'Liberia' 'Libya' 'Liechtenstein' 'Lithuania' 'Low income'
 'Lower middle income' 'Luxembourg' 'Macao' 'Madagascar' 'Malawi'
 'Malaysia' 'Maldives' 'Mali' 'Malta' 'Marshall Islands' 'Martinique'
 'Mauritania' 'Mauritius' 'Mayotte' 'Mexico' 'Micronesia (country)'
 'Moldova' 'Monaco' 'Mongolia' 'Montenegro' 'Montserrat' 'Morocco'
 'Mozambique' 'Myanmar' 'Namibia' 'Nauru' 'Nepal' 'Netherlands'
 'New Caledonia' 'New Zealand' 'Nicaragua' 'Niger' 'Nigeria' 'Niue'
 'North America' 'North Korea' 'North Macedonia' 'Northern Cyprus'
 'Northern Ireland' 'Northern Mariana Islands' 'Norway' 'Oceania' 'Oman'
 'Pakistan' 'Palau' 'Palestine' 'Panama' 'Papua New Guinea' 'Paraguay'
 'Peru' 'Philippines' 'Pitcairn' 'Poland' 'Portugal' 'Puerto Rico' 'Qatar'
 'Reunion' 'Romania' 'Russia' 'Rwanda' 'Saint Barthelemy' 'Saint Helena'
 'Saint Kitts and Nevis' 'Saint Lucia' 'Saint Martin (French part)'
 'Saint Pierre and Miquelon' 'Saint Vincent and the Grenadines' 'Samoa'
 'San Marino' 'Sao Tome and Principe' 'Saudi Arabia' 'Scotland' 'Senegal'
 'Serbia' 'Seychelles' 'Sierra Leone' 'Singapore'
 'Sint Maarten (Dutch part)' 'Slovakia' 'Slovenia' 'Solomon Islands'
 'Somalia' 'South Africa' 'South America' 'South Korea' 'South Sudan'
 'Spain' 'Sri Lanka' 'Sudan' 'Suriname' 'Sweden' 'Switzerland' 'Syria'
 'Taiwan' 'Tajikistan' 'Tanzania' 'Thailand' 'Timor' 'Togo' 'Tokelau'
 'Tonga' 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Turkmenistan'
 'Turks and Caicos Islands' 'Tuvalu' 'Uganda' 'Ukraine'
 'United Arab Emirates' 'United Kingdom' 'United States'
 'United States Virgin Islands' 'Upper middle income' 'Uruguay'
 'Uzbekistan' 'Vanuatu' 'Vatican' 'Venezuela' 'Vietnam' 'Wales'
 'Wallis and Futuna' 'Western Sahara' 'World' 'Yemen' 'Zambia' 'Zimbabwe']
255


# Delete World and Continent Summary Figures
df.drop(df[df["continent"].isna()].index, inplace=True)

# Get most recent date
df_most_recent = df[df["date"]==max(df["date"])].reset_index()
df_most_recent


df_most_recent_useful = df[df["total_cases"].notnull()].reset_index()
df_most_recent_useful


df_most_recent = df[df["date"] == "2023-06-21"].reset_index()
df_most_recent


# Need to delete the World and continent summary figures
df_most_recent


df_world = df_most_recent.groupby("date")[["total_cases", "new_cases", "total_deaths"]].sum().reset_index()
df_world


# Create Column headers/labels
labels = ["Last Update", "Total Confirmed", "New Cases", "Total Deaths"]

fig = go.Figure(data=[go.Table(header = dict(values=labels),
                              cells = dict(values=df_world.loc[0, ["date", "total_cases", "new_cases", "total_deaths"]]))])

fig.update_layout(title="Covid-19 World Summary: ")
fig.show()


df_over_time = df.groupby("date")[["total_cases", "new_cases", "total_deaths"]].sum().reset_index().sort_values("date", ascending=True).reset_index(drop=True)
df_over_time


df_over_time["date"] = pd.to_datetime(df_over_time["date"])


df_over_time = df_over_time[df_over_time["date"] <= "2023-06-21"]


df_over_time


fig = go.Figure()
fig.add_trace(go.Scatter(x=df_over_time["date"], y=df_over_time["total_cases"],
                        mode = "lines",
                        name = "Confirmed Cases"))

fig.update_layout(
    title="Evolution of Confirmed Global Covid-19 Cases Over Time",
    template="plotly_white",
    yaxis_title="Confirmed Cases",
    xaxis_title="Date",
)

fig.update_xaxes(
    dtick="M1",
    tickformat="%b-%Y",
)

fig.show()


fig = go.Figure()

fig.add_trace(go.Scatter(x=df_over_time["date"], y=df_over_time["total_deaths"],
                        mode="lines", marker_color="red",
                        name="Total Deaths", line=dict(dash="dot")))

fig.update_layout(
    title="Evolution of Global Covid-19 Deaths Over Time",
    template="plotly_dark",
    yaxis_title="Number of Deaths",
    xaxis_title="Date",
)


fig.update_xaxes(
    dtick="M1",
    tickformat="%b-%Y",
)


fig.show()


fig = go.Figure()

fig.add_trace(go.Scatter(x=df_over_time["date"], y=df_over_time["new_cases"],
                        mode="lines", marker_color="orange",
                        name="Total Deaths",))

fig.update_layout(
    title="Evolution of Global Covid-19 New Cases Over Time",
    template="plotly_dark",
    yaxis_title="Number of New Cases",
    xaxis_title="Date",
    width=2000,
    height=600
)


fig.update_xaxes(
    dtick="M1",
    tickformat="%b-%Y",
)


fig.show()


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 308332 entries, 0 to 323699
Data columns (total 67 columns):
 #   Column                                      Non-Null Count   Dtype  
---  ------                                      --------------   -----  
 0   iso_code                                    308332 non-null  object 
 1   continent                                   308332 non-null  object 
 2   location                                    308332 non-null  object 
 3   date                                        308332 non-null  object 
 4   total_cases                                 271407 non-null  float64
 5   new_cases                                   299422 non-null  float64
 6   new_cases_smoothed                          298223 non-null  float64
 7   total_deaths                                250519 non-null  float64
 8   new_deaths                                  299464 non-null  float64
 9   new_deaths_smoothed                         298294 non-null  float64
 10  total_cases_per_million                     271407 non-null  float64
 11  new_cases_per_million                       299422 non-null  float64
 12  new_cases_smoothed_per_million              298223 non-null  float64
 13  total_deaths_per_million                    250519 non-null  float64
 14  new_deaths_per_million                      299464 non-null  float64
 15  new_deaths_smoothed_per_million             298294 non-null  float64
 16  reproduction_rate                           183741 non-null  float64
 17  icu_patients                                36791 non-null   float64
 18  icu_patients_per_million                    36791 non-null   float64
 19  hosp_patients                               37673 non-null   float64
 20  hosp_patients_per_million                   37673 non-null   float64
 21  weekly_icu_admissions                       9721 non-null    float64
 22  weekly_icu_admissions_per_million           9721 non-null    float64
 23  weekly_hosp_admissions                      22425 non-null   float64
 24  weekly_hosp_admissions_per_million          22425 non-null   float64
 25  total_tests                                 79387 non-null   float64
 26  new_tests                                   75403 non-null   float64
 27  total_tests_per_thousand                    79387 non-null   float64
 28  new_tests_per_thousand                      75403 non-null   float64
 29  new_tests_smoothed                          103965 non-null  float64
 30  new_tests_smoothed_per_thousand             103965 non-null  float64
 31  positive_rate                               95927 non-null   float64
 32  tests_per_case                              94348 non-null   float64
 33  tests_units                                 106788 non-null  object 
 34  total_vaccinations                          65585 non-null   float64
 35  people_vaccinated                           62338 non-null   float64
 36  people_fully_vaccinated                     59049 non-null   float64
 37  total_boosters                              35211 non-null   float64
 38  new_vaccinations                            52092 non-null   float64
 39  new_vaccinations_smoothed                   161954 non-null  float64
 40  total_vaccinations_per_hundred              65585 non-null   float64
 41  people_vaccinated_per_hundred               62338 non-null   float64
 42  people_fully_vaccinated_per_hundred         59049 non-null   float64
 43  total_boosters_per_hundred                  35211 non-null   float64
 44  new_vaccinations_smoothed_per_million       161954 non-null  float64
 45  new_people_vaccinated_smoothed              161745 non-null  float64
 46  new_people_vaccinated_smoothed_per_hundred  161745 non-null  float64
 47  stringency_index                            197651 non-null  float64
 48  population_density                          273404 non-null  float64
 49  median_age                                  254189 non-null  float64
 50  aged_65_older                               245244 non-null  float64
 51  aged_70_older                               251629 non-null  float64
 52  gdp_per_capita                              249084 non-null  float64
 53  extreme_poverty                             160012 non-null  float64
 54  cardiovasc_death_rate                       249598 non-null  float64
 55  diabetes_prevalence                         262369 non-null  float64
 56  female_smokers                              186894 non-null  float64
 57  male_smokers                                184334 non-null  float64
 58  handwashing_facilities                      121605 non-null  float64
 59  hospital_beds_per_thousand                  220174 non-null  float64
 60  life_expectancy                             296429 non-null  float64
 61  human_development_index                     241889 non-null  float64
 62  population                                  308332 non-null  float64
 63  excess_mortality_cumulative_absolute        11245 non-null   float64
 64  excess_mortality_cumulative                 11245 non-null   float64
 65  excess_mortality                            11245 non-null   float64
 66  excess_mortality_cumulative_per_million     11245 non-null   float64
dtypes: float64(62), object(5)
memory usage: 160.0+ MB


df["date"] = pd.to_datetime(df["date"])


df_per_country = df.groupby("location")[["new_cases", "new_deaths"]].sum().reset_index().sort_values("new_cases", ascending=False).reset_index(drop=True)
df_per_country


df_per_country.columns = ["Country", "Total Cases", "Total Deaths"]
df_per_country = df_per_country.astype({"Country":str, "Total Cases":int, "Total Deaths":int})
df_per_country.head(10)


fig = go.Figure(go.Bar(
            x=df_per_country["Total Cases"],
            y=df_per_country["Country"],
            orientation="h"))

fig.update_layout(
    title="Confirmed Cases in Each Country",
    template="plotly_white",
    xaxis_title="Confirmed Cases",
    yaxis_title="Country",

)

fig.show()


fig = go.Figure(go.Bar(
            x=df_per_country["Total Deaths"],
            y=df_per_country["Country"],
            orientation="h",
            marker_color="black"))

fig.update_layout(
    title="Confirmed Deaths in Each Country ",
    template="plotly_white",
    xaxis_title="Confirmed Deaths",
    yaxis_title="Country",

)

fig.show()


fig = px.choropleth(df_per_country, locations=df_per_country["Country"],
                   color=df_per_country["Total Cases"], locationmode="country names",
                   hover_name=df_per_country["Country"],
                   color_continuous_scale=px.colors.sequential.OrRd,
                   template="plotly_dark", )

fig.update_layout(
    title="Confirmed Cases in Each Country",
    margin=dict(l=0, r=0, t=40, b=0)
)

fig.show()


df_per_country_pm = df.groupby("location")[["new_cases_per_million", "new_deaths_per_million"]].sum().reset_index().sort_values("new_cases_per_million", ascending=False).reset_index(drop=True)
df_per_country_pm.columns = ["Country", "Total Cases PM", "Total Deaths PM"]
df_per_country_pm = df_per_country_pm.astype({"Country":str, "Total Cases PM":int, "Total Deaths PM":int})
df_per_country_pm.head(10)


fig = px.choropleth(df_per_country_pm, locations=df_per_country_pm["Country"],
                   color=df_per_country_pm["Total Cases PM"], locationmode="country names",
                   hover_name=df_per_country_pm["Country"],
                   color_continuous_scale=px.colors.sequential.Viridis,
                   template="plotly_dark", )

fig.update_layout(
    title="Confirmed Cases Per Million in Each Country",
    margin=dict(l=0, r=0, t=40, b=0)
)

fig.show()


fig = go.Figure(data=[go.Scatter(
    x=df_per_country_pm["Country"][0:10],
    y=df_per_country_pm["Total Cases PM"][0:10],
    mode="markers",
    marker=dict(
        color=100+np.random.randn(500),
        size=df_per_country_pm["Total Cases PM"][0:10]/10000,
        showscale=True
        )
)])

fig.update_layout(
    title="Top 10 Most Infected Countries Per Million",
    xaxis_title="Country",
    yaxis_title="Total Cases PM",
    template="plotly_white",
    width=800
)

fig.show()


df.tail()


df_sorted = df.copy()
df_sorted = df.groupby(["date", "location",])[["total_cases"]].sum().reset_index().sort_values("date", ascending=True).reset_index(drop=True)
df_sorted = df_sorted[df_sorted["date"] <= "2023-06-21"] # Excluding very recent dates with no useful data

# Need to convert timestamp dates back to string form as timestamp objects are not compatible with plotly interactive map
df_sorted["date"] = df_sorted["date"].astype(str)


fig = px.choropleth(df_sorted, locations=df_sorted["location"],
                   color=df_sorted["total_cases"], locationmode="country names",
                   hover_name=df_sorted["location"],
                   color_continuous_scale=px.colors.sequential.Inferno,
                   animation_frame="date")

fig.update_layout(

    title="Evolution of Confirmed Cases in Each Country",
    template="plotly_dark",
    legend_title_text="Total Cases",
)

fig.show()


df_sorted_pm = df.copy()
df_sorted_pm = df.groupby(["date", "location",])[["total_cases_per_million"]].sum().reset_index().sort_values("date", ascending=True).reset_index(drop=True)
df_sorted_pm = df_sorted_pm[df_sorted_pm["date"] <= "2023-06-21"] # Excluding very recent dates with no useful data

# Need to convert timestamp dates back to string form as timestamp objects are not compatible with plotly interactive map
df_sorted_pm["date"] = df_sorted_pm["date"].astype(str)


fig = px.choropleth(df_sorted_pm, locations=df_sorted_pm["location"],
                   color=df_sorted_pm["total_cases_per_million"], locationmode="country names",
                   hover_name=df_sorted_pm["location"],
                   color_continuous_scale=px.colors.sequential.Inferno,
                   animation_frame="date")

fig.update_layout(

    title="Evolution of Confirmed Cases Per Million in Each Country",
    template="plotly_dark",
    legend_title_text="Total Cases Per Million",
                   
)

fig.show()


df_per_continent_pm = df.groupby("continent")[["new_cases_per_million", "new_deaths_per_million"]].sum().reset_index().sort_values("new_cases_per_million", ascending=False).reset_index(drop=True)
df_per_continent_pm.columns = ["Continent", "Total Cases PM", "Total Deaths PM"]
df_per_continent_pm = df_per_continent_pm.astype({"Continent":str, "Total Cases PM":int, "Total Deaths PM":int})
df_per_continent_pm


fig = go.Figure(data=[go.Scatter(
    x=df_per_continent_pm["Continent"],
    y=df_per_continent_pm["Total Cases PM"],
    mode="markers",
    marker=dict(
        color=100+np.random.randn(500),
        size=df_per_continent_pm["Total Cases PM"][0:10]/100000,
        showscale=True
        )
)])

fig.update_layout(
    title="Top 10 Most Infected Continents Per Million",
    xaxis_title="Continent",
    yaxis_title="Total Cases PM",
    template="plotly_white",
    width=800
)


df_usa = df[df["location"] == "United States"]
df_uk = df[df["location"] == "United Kingdom"]


fig = go.Figure()

fig.add_trace(go.Scatter(x=df_usa["date"], y=df_usa["total_cases_per_million"],
                        mode="lines",
                        name="United States"))

fig.add_trace(go.Scatter(x=df_uk["date"], y=df_uk["total_cases_per_million"],
                        mode="lines",
                        name="United Kingdom"))

fig.update_layout(
    title="Evolution of Confirmed Cases PM Over Time in United States vs United Kingdom",
    template="plotly_white",
    xaxis_title="Date",
    yaxis_title="Confirmed Cases PM",
)

fig.show()


df_india = df[df["location"] == "India"]

fig = go.Figure()

fig.add_trace(go.Scatter(x=df_uk["date"], y=df_uk["total_cases_per_million"],
                        mode="lines",
                        name="United Kingdom"))

fig.add_trace(go.Scatter(x=df_india["date"], y=df_india["total_cases_per_million"],
                        mode="lines",
                        name="India"))

fig.update_layout(
    title="Evolution of Confirmed Cases PM Over Time in United Kingdom vs India",
    template="plotly_white",
    xaxis_title="Date",
    yaxis_title="Confirmed Cases PM",
)

fig.show()


fig = go.Figure()

fig.add_trace(go.Scatter(x=df_usa["date"], y=df_usa["total_deaths_per_million"],
                        mode="lines",
                        name="United States"))

fig.add_trace(go.Scatter(x=df_uk["date"], y=df_uk["total_deaths_per_million"],
                        mode="lines",
                        name="United Kingdom"))

fig.update_layout(
    title="Evolution of Confirmed Deaths PM Over Time in United States vs United Kingdom",
    template="plotly_white",
    xaxis_title="Date",
    yaxis_title="Confirmed Deaths PM",
)

fig.show()


df_india = df[df["location"] == "India"]

fig = go.Figure()

fig.add_trace(go.Scatter(x=df_uk["date"], y=df_uk["total_deaths_per_million"],
                        mode="lines",
                        name="United Kingdom"))

fig.add_trace(go.Scatter(x=df_india["date"], y=df_india["total_deaths_per_million"],
                        mode="lines",
                        name="India"))

fig.update_layout(
    title="Evolution of Confirmed Deaths PM Over Time in United Kingdom vs India",
    template="plotly_white",
    xaxis_title="Date",
    yaxis_title="Confirmed Deaths PM",
)

fig.show()


url = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv"
df = pd.read_csv(url)
df.head()


# Convert date from str to datetime obj
df["date"] = pd.to_datetime(df["date"])


# Remove all dates after 2023-06-21
df = df[df["date"] <= "2023-06-21"]


df["date"] = df["date"].dt.strftime("%Y/%m/%d")
df


df.set_index("date", inplace=True)
df


# Create the pivot table
df_flourish = pd.pivot_table(df, values="total_cases", index=["location"], columns="date").reset_index()
df_flourish = df_flourish.fillna(0)

df_flourish.head()


file_name = "https://raw.githubusercontent.com/rajeevratan84/covid/master/flags_world.csv"
flags = pd.read_csv(file_name)
flags


df_flourish = pd.merge(df_flourish, flags, left_on="location", right_on="country")
df_flourish.head()


from datetime import datetime

# Use curreent date for timestamped filename
datetime_obj = datetime.now()
timestamp_str = datetime_obj.strftime("%d_%b_%Y")

# Delete unnecessary columns
del df_flourish["country"]

# Create a series to hold the Image URLs
mid = df_flourish["Image URL"]

# Delete the existing Image URLs
df_flourish.drop(labels=["Image URL"], axis=1, inplace=True)

# Insert Mid into the second position in the dataframe
df_flourish.insert(1, "Image URL", mid)

# Give the CSV a name with the current timestamp
csv_name = "world_covid_cases_" + timestamp_str + ".csv"
df_flourish.to_csv(csv_name)
df_flourish.head()

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	population	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	AFG	Asia	Afghanistan	2020-01-03	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
1	AFG	Asia	Afghanistan	2020-01-04	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
2	AFG	Asia	Afghanistan	2020-01-05	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
3	AFG	Asia	Afghanistan	2020-01-06	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
4	AFG	Asia	Afghanistan	2020-01-07	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
323695	ZWE	Africa	Zimbabwe	2023-07-01	265524.0	0.0	15.857	5707.0	0.0	0.000	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323696	ZWE	Africa	Zimbabwe	2023-07-02	265524.0	0.0	15.857	5707.0	0.0	0.000	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323697	ZWE	Africa	Zimbabwe	2023-07-03	265604.0	80.0	11.429	5709.0	2.0	0.286	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323698	ZWE	Africa	Zimbabwe	2023-07-04	265604.0	0.0	11.429	5709.0	0.0	0.286	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323699	ZWE	Africa	Zimbabwe	2023-07-05	265604.0	0.0	11.429	5709.0	0.0	0.286	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN

	index	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	population	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	24323	BGD	Asia	Bangladesh	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	44.7	34.808	0.800	72.59	0.632	1.711864e+08	NaN	NaN	NaN	NaN
1	43524	BGR	Europe	Bulgaria	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	44.4	NaN	7.454	75.05	0.816	6.781955e+06	NaN	NaN	NaN	NaN
2	71685	CZE	Europe	Czechia	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	38.3	NaN	6.630	79.38	0.900	1.049399e+07	NaN	NaN	NaN	NaN
3	110009	GRC	Europe	Greece	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	52.0	NaN	4.210	82.24	0.888	1.038497e+07	NaN	NaN	NaN	NaN
4	130446	IND	Asia	India	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	20.6	59.550	0.530	69.66	0.645	1.417173e+09	NaN	NaN	NaN	NaN
5	152207	KGZ	Asia	Kyrgyzstan	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	50.5	89.220	4.500	71.45	0.697	6.630621e+06	NaN	NaN	NaN	NaN
6	170924	MYS	Asia	Malaysia	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	42.4	NaN	1.900	76.16	0.810	3.393822e+07	NaN	NaN	NaN	NaN
7	199087	NLD	Europe	Netherlands	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	27.3	NaN	3.320	82.28	0.944	1.756402e+07	NaN	NaN	NaN	NaN
8	279000	SWE	Europe	Sweden	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	18.9	NaN	2.220	82.80	0.945	1.054935e+07	NaN	NaN	NaN	NaN
9	309706	URY	South America	Uruguay	2023-07-06	NaN	NaN	NaN	NaN	NaN	...	19.9	NaN	2.800	77.91	0.817	3.422796e+06	NaN	NaN	NaN	NaN

	index	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	population	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	54	AFG	Asia	Afghanistan	2020-02-26	1.0	1.0	0.143	NaN	0.0	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
1	55	AFG	Asia	Afghanistan	2020-02-27	1.0	0.0	0.143	NaN	0.0	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
2	56	AFG	Asia	Afghanistan	2020-02-28	1.0	0.0	0.143	NaN	0.0	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
3	57	AFG	Asia	Afghanistan	2020-02-29	1.0	0.0	0.143	NaN	0.0	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
4	58	AFG	Asia	Afghanistan	2020-03-01	1.0	0.0	0.143	NaN	0.0	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
271402	323695	ZWE	Africa	Zimbabwe	2023-07-01	265524.0	0.0	15.857	5707.0	0.0	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
271403	323696	ZWE	Africa	Zimbabwe	2023-07-02	265524.0	0.0	15.857	5707.0	0.0	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
271404	323697	ZWE	Africa	Zimbabwe	2023-07-03	265604.0	80.0	11.429	5709.0	2.0	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
271405	323698	ZWE	Africa	Zimbabwe	2023-07-04	265604.0	0.0	11.429	5709.0	0.0	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
271406	323699	ZWE	Africa	Zimbabwe	2023-07-05	265604.0	0.0	11.429	5709.0	0.0	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN

	index	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	population	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	1265	AFG	Asia	Afghanistan	2023-06-21	223059.0	38.0	40.714	7924.0	0.0	...	NaN	37.746	0.50	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
1	3825	ALB	Europe	Albania	2023-06-21	334090.0	0.0	0.000	3604.0	0.0	...	51.2	NaN	2.89	78.57	0.795	2842318.0	NaN	NaN	NaN	NaN
2	5105	DZA	Africa	Algeria	2023-06-21	271847.0	0.0	0.000	6881.0	0.0	...	30.4	83.741	1.90	76.88	0.748	44903228.0	NaN	NaN	NaN	NaN
3	6385	ASM	Oceania	American Samoa	2023-06-21	8332.0	0.0	0.000	34.0	0.0	...	NaN	NaN	NaN	73.74	NaN	44295.0	NaN	NaN	NaN	NaN
4	7665	AND	Europe	Andorra	2023-06-21	48015.0	0.0	0.000	159.0	0.0	...	37.8	NaN	NaN	83.73	0.868	79843.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
234	317283	OWID_WLS	Europe	Wales	2023-06-21	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	3170000.0	NaN	NaN	NaN	NaN
235	318563	WLF	Oceania	Wallis and Futuna	2023-06-21	3550.0	0.0	0.000	8.0	0.0	...	NaN	NaN	NaN	79.94	NaN	11596.0	NaN	NaN	NaN	NaN
236	321125	YEM	Asia	Yemen	2023-06-21	11945.0	0.0	0.000	2159.0	0.0	...	29.2	49.542	0.70	66.12	0.470	33696612.0	NaN	NaN	NaN	NaN
237	322405	ZMB	Africa	Zambia	2023-06-21	345961.0	0.0	129.000	4060.0	0.0	...	24.7	13.938	2.00	63.89	0.584	20017670.0	NaN	NaN	NaN	NaN
238	323685	ZWE	Africa	Zimbabwe	2023-06-21	265413.0	0.0	17.714	5707.0	0.0	...	30.7	36.791	1.70	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN

	index	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	population	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	1265	AFG	Asia	Afghanistan	2023-06-21	223059.0	38.0	40.714	7924.0	0.0	...	NaN	37.746	0.50	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
1	3825	ALB	Europe	Albania	2023-06-21	334090.0	0.0	0.000	3604.0	0.0	...	51.2	NaN	2.89	78.57	0.795	2842318.0	NaN	NaN	NaN	NaN
2	5105	DZA	Africa	Algeria	2023-06-21	271847.0	0.0	0.000	6881.0	0.0	...	30.4	83.741	1.90	76.88	0.748	44903228.0	NaN	NaN	NaN	NaN
3	6385	ASM	Oceania	American Samoa	2023-06-21	8332.0	0.0	0.000	34.0	0.0	...	NaN	NaN	NaN	73.74	NaN	44295.0	NaN	NaN	NaN	NaN
4	7665	AND	Europe	Andorra	2023-06-21	48015.0	0.0	0.000	159.0	0.0	...	37.8	NaN	NaN	83.73	0.868	79843.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
234	317283	OWID_WLS	Europe	Wales	2023-06-21	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	3170000.0	NaN	NaN	NaN	NaN
235	318563	WLF	Oceania	Wallis and Futuna	2023-06-21	3550.0	0.0	0.000	8.0	0.0	...	NaN	NaN	NaN	79.94	NaN	11596.0	NaN	NaN	NaN	NaN
236	321125	YEM	Asia	Yemen	2023-06-21	11945.0	0.0	0.000	2159.0	0.0	...	29.2	49.542	0.70	66.12	0.470	33696612.0	NaN	NaN	NaN	NaN
237	322405	ZMB	Africa	Zambia	2023-06-21	345961.0	0.0	129.000	4060.0	0.0	...	24.7	13.938	2.00	63.89	0.584	20017670.0	NaN	NaN	NaN	NaN
238	323685	ZWE	Africa	Zimbabwe	2023-06-21	265413.0	0.0	17.714	5707.0	0.0	...	30.7	36.791	1.70	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN

Covid-19 Data Analysis¶

Imports¶

Load and Explore Data¶

Quick Summary of the Missing Data¶

Unique Locations¶

Covid-19 World Analysis - Most Recent Info¶

Create a Summary Table¶

World Covid-19 Growth Over Time¶

Confirmed Cases Starting from Day Zero (First Covid Case Reported)¶

Confirmed Global Covid-19 Deaths Over Time¶

Confirmed Global Covid-19 New Cases Over Time¶

Confirmed Cases in Each Country¶

Mapping Covid-19 Across the World¶

Adjusting for Population Size¶

Let's look at the top 10 countries for confirmed cases per million on a scatter plot¶

Evolution of Confirmed Cases¶

Comparing Continents and Countries¶

First let's compare USA vs the United Kingdom¶

Now let's compare United Kingdom vs the India¶

Let's do the same plots as above but for total deaths per million¶

Create a Dataset to Import into Flourish¶

Reload the Data¶

Get Flag Icons for the Flourish Chart¶

Merging our data with the flag URL dataset¶

Generating our final file for Flourish¶

Link to the Flourish Chart Race Visualisation¶

	Column Name	Percentage
0	iso_code	0.000000
1	continent	4.747606
2	location	0.000000
3	date	0.000000
4	total_cases	11.476985
5	new_cases	2.755020
6	new_cases_smoothed	3.143960
7	total_deaths	17.976213
8	new_deaths	2.742045
9	new_deaths_smoothed	3.122027
10	total_cases_per_million	11.476985
11	new_cases_per_million	2.755020
12	new_cases_smoothed_per_million	3.143960
13	total_deaths_per_million	17.976213
14	new_deaths_per_million	2.742045
15	new_deaths_smoothed_per_million	3.122027
16	reproduction_rate	42.904850
17	icu_patients	88.634229
18	icu_patients_per_million	88.634229
19	hosp_patients	88.361755
20	hosp_patients_per_million	88.361755
21	weekly_icu_admissions	96.996911
22	weekly_icu_admissions_per_million	96.996911
23	weekly_hosp_admissions	93.072289
24	weekly_hosp_admissions_per_million	93.072289
25	total_tests	75.475131
26	new_tests	76.705901
27	total_tests_per_thousand	75.475131
28	new_tests_per_thousand	76.705901
29	new_tests_smoothed	67.882298
30	new_tests_smoothed_per_thousand	67.882298
31	positive_rate	70.365462
32	tests_per_case	70.853259
33	tests_units	67.010195
34	total_vaccinations	76.339821
35	people_vaccinated	77.342910
36	people_fully_vaccinated	78.416435
37	total_boosters	86.079086
38	new_vaccinations	80.524560
39	new_vaccinations_smoothed	46.585110
40	total_vaccinations_per_hundred	76.339821
41	people_vaccinated_per_hundred	77.342910
42	people_fully_vaccinated_per_hundred	78.416435
43	total_boosters_per_hundred	86.079086
44	new_vaccinations_smoothed_per_million	46.585110
45	new_people_vaccinated_smoothed	46.649676
46	new_people_vaccinated_smoothed_per_hundred	46.649676
47	stringency_index	38.940068
48	population_density	15.142107
49	median_age	21.078159
50	aged_65_older	23.841520
51	aged_70_older	21.869015
52	gdp_per_capita	22.655236
53	extreme_poverty	50.172073
54	cardiovasc_death_rate	22.496447
55	diabetes_prevalence	18.551128
56	female_smokers	41.867470
57	male_smokers	42.658326
58	handwashing_facilities	62.037071
59	hospital_beds_per_thousand	31.586345
60	life_expectancy	8.029039
61	human_development_index	24.877973
62	population	0.000000
63	excess_mortality_cumulative_absolute	96.526104
64	excess_mortality_cumulative	96.526104
65	excess_mortality	96.526104
66	excess_mortality_cumulative_per_million	96.526104

	date	total_cases	new_cases	total_deaths
0	2020-01-01	0.0	0.0	0.0
1	2020-01-02	0.0	0.0	0.0
2	2020-01-03	0.0	0.0	0.0
3	2020-01-04	3.0	3.0	0.0
4	2020-01-05	3.0	0.0	3.0
...	...	...	...	...
1278	2023-07-02	767582199.0	18639.0	6948254.0
1279	2023-07-03	767725633.0	143434.0	6948743.0
1280	2023-07-04	767726097.0	464.0	6948751.0
1281	2023-07-05	767726097.0	0.0	6948751.0
1282	2023-07-06	0.0	0.0	0.0

	location	new_cases	new_deaths
0	United States	103436829.0	1129589.0
1	China	99292081.0	121490.0
2	India	44995105.0	531908.0
3	France	38989382.0	167923.0
4	Germany	38435774.0	174807.0
...	...	...	...
238	Turkmenistan	0.0	0.0
239	Western Sahara	0.0	0.0
240	Taiwan	0.0	0.0
241	North Korea	0.0	0.0
242	Northern Ireland	0.0	0.0

	Country	Total Cases	Total Deaths
0	United States	103436829	1129589
1	China	99292081	121490
2	India	44995105	531908
3	France	38989382	167923
4	Germany	38435774	174807
5	Brazil	37671993	703964
6	Japan	33803572	74708
7	South Korea	32256154	35078
8	Italy	25897801	190868
9	United Kingdom	24639160	227739

	Country	Total Cases PM	Total Deaths PM
0	Cyprus	737554	1522
1	San Marino	722054	3710
2	Brunei	687696	358
3	Austria	680262	2520
4	Faeroe Islands	652484	527
5	Slovenia	634272	4439
6	Gibraltar	628882	3458
7	Martinique	626793	3003
8	South Korea	622515	676
9	France	603302	2598

	Continent	Total Cases PM	Total Deaths PM
0	Europe	20247965	132916
1	North America	9476067	62005
2	Asia	6658171	31855
3	Oceania	5490325	12800
4	Africa	2646608	18065
5	South America	2562786	33152

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	population	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	AFG	Asia	Afghanistan	2020/01/03	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
1	AFG	Asia	Afghanistan	2020/01/04	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
2	AFG	Asia	Afghanistan	2020/01/05	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
3	AFG	Asia	Afghanistan	2020/01/06	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
4	AFG	Asia	Afghanistan	2020/01/07	NaN	0.0	NaN	NaN	0.0	NaN	...	NaN	37.746	0.5	64.83	0.511	41128772.0	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
323681	ZWE	Africa	Zimbabwe	2023/06/17	265289.0	0.0	18.286	5702.0	0.0	1.000	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323682	ZWE	Africa	Zimbabwe	2023/06/18	265289.0	0.0	18.286	5702.0	0.0	1.000	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323683	ZWE	Africa	Zimbabwe	2023/06/19	265413.0	124.0	17.714	5707.0	5.0	0.714	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323684	ZWE	Africa	Zimbabwe	2023/06/20	265413.0	0.0	17.714	5707.0	0.0	0.714	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN
323685	ZWE	Africa	Zimbabwe	2023/06/21	265413.0	0.0	17.714	5707.0	0.0	0.714	...	30.7	36.791	1.7	61.49	0.571	16320539.0	NaN	NaN	NaN	NaN

date	location	...	2023/06/12	2023/06/13	2023/06/14	2023/06/15	2023/06/16	2023/06/17	2023/06/18	2023/06/19	2023/06/20	2023/06/21
0	Afghanistan	...	222603.0	222725.0	222774.0	222845.0	222871.0	222952.0	222959.0	222990.0	223021.0	223059.0
1	Africa	...	13096162.0	13096169.0	13096170.0	13096202.0	13096215.0	13097204.0	13097236.0	13097986.0	13097988.0	13097988.0
2	Albania	...	334090.0	334090.0	334090.0	334090.0	334090.0	334090.0	334090.0	334090.0	334090.0	334090.0
3	Algeria	...	271847.0	271847.0	271847.0	271847.0	271847.0	271847.0	271847.0	271847.0	271847.0	271847.0
4	American Samoa	...	8332.0	8332.0	8332.0	8332.0	8332.0	8332.0	8332.0	8332.0	8332.0	8332.0

	country	Image URL
0	Afghanistan	https://upload.wikimedia.org/wikipedia/commons...
1	Angola	https://cdn.countryflags.com/thumbs/angola/fla...
2	Albania	https://cdn.countryflags.com/thumbs/albania/fl...
3	Andorra	https://cdn.countryflags.com/thumbs/andorra/fl...
4	Argentina	https://cdn.countryflags.com/thumbs/argentina/...
...	...	...
203	Kosovo	https://cdn.countryflags.com/thumbs/kosovo/fla...
204	Yemen, Rep.	https://flagpedia.net/data/flags/w580/ye.png
205	South Africa	https://cdn.countryflags.com/thumbs/south-afri...
206	Zambia	https://cdn.countryflags.com/thumbs/zambia/fla...
207	Zimbabwe	https://cdn.countryflags.com/thumbs/zimbabwe/f...